- Poster presentation
- Open Access
Bridging spiking neuron models and mesoscopic population models - a general theory for neural population dynamics
© Schwalger et al. 2015
- Published: 18 December 2015
- Neuron Model
- Synaptic Conductance
- Cortical Circuit
- Real Neuron
- Conductance Dynamic
Many circuits of the brain can be described by a system of interacting neural populations that are approximately homogeneous. For instance, cortical layers typically consist of a few main types of excitatory and inhibitory neurons that form small homogeneous populations of neurons. Such systems can be modeled on different spatial scales. On the microscopic scale, single cell activity has been faithfully described by reduced phenomenological neuron models . Simulations of networks of such neuron models are, however, computationally expensive and do not offer much analytical insight. On the other hand, mesoscopic population models are reduced descriptions of the global activities of each population. These activities are stochastic due to the finite sizes of the populations. Mesoscopic models can be efficiently simulated and provide a better understanding of the dynamics owing to the abstraction of microscopic information. However, it is largely unknown how to relate mesoscopic population models to microscopic properties such as neural refractoriness, synaptic conductance dynamics and spike-frequency adaptation.
Research was supported by the European Research Council (Grant Agreement no. 268689, MultiRules)
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